jams-data-analysis

Research

Use when running and reporting the analysis for a Journal of the Academy of Marketing Science (JAMS) manuscript — selecting the estimator that matches the design (SEM/PLS, HLM, regression/econometrics, experiments, meta-analysis), reporting effect sizes and uncertainty, and translating estimates into managerial magnitudes. Executes and reports; jams-methods designs the study and jams-contribution-framing states the payoff.

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Source SKILL.md: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/Journal-of-the-Academy-of-Marketing-Science-Skills/skills/jams-data-analysis/SKILL.md

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Data Analysis & Reporting (jams-data-analysis)

When to trigger

Data are collected and it is time to estimate and report
You are unsure whether the estimator matches the design or the data structure
A reviewer says "the analysis does not support the inference" or "report effect sizes"
Significance is reported but the managerial magnitude is missing

Choose the estimator that matches the design

Design / claim	Estimator
Latent constructs + structural paths (survey)	Covariance-based SEM (Mplus / lavaan / AMOS); PLS-SEM when prediction or formative constructs dominate
Nested data (consumers in stores, firms in industries)	HLM / multilevel models; random intercepts/slopes; report ICC
Mediation (process)	Bootstrapped indirect effects (PROCESS / lavaan), bias-corrected CIs; report the indirect effect, not just Baron–Kenny steps
Moderation / moderated mediation	Interaction term + simple slopes; conditional indirect effects (index of moderated mediation)
Experiment (factorial)	ANOVA / regression; estimated marginal means; planned contrasts; effect sizes per cell
Panel / observational causal	FE / DiD (modern staggered estimators); cluster-robust SE
Endogenous marketing regressor	IV/2SLS or Gaussian-copula control function; report first stage / instrument strength
Discrete choice / demand	Logit/probit; random-coefficient (mixed) logit
Meta-analysis	Random-effects effect-size synthesis; moderator meta-regression; publication-bias diagnostics

Match SE clustering to the sampling/assignment structure (participant, store, market, firm).

JAMS reporting conventions

APA results style. Report exact statistics (coefficients, SEs or t-values, CIs, exact p where shown). Avoid asterisk-only tables where the journal asks for precision; let the magnitude, not the star count, carry the result.
Effect sizes and uncertainty, always. Standardized coefficients, R²/f², η²/Cohen's d, or odds ratios as the model requires — significance without magnitude is not a JAMS result.
SEM reporting: measurement model first (loadings, AVE, CR, discriminant validity), then the structural model (standardized paths, R² for endogenous constructs, overall fit: CFI, TLI, RMSEA, SRMR).
PLS reporting: loadings/weights, CR, AVE, HTMT, R², Q² (predictive relevance), and f²; bootstrap the path significances.

Translate every result into a managerial magnitude

This is the JAMS-distinguishing step. For each headline result, write a ledger row before drafting the results paragraph:

Result	Theory point it supports	Required statistic	Managerial magnitude
Main path / treatment effect	which hypothesis / mechanism is confirmed	std. coef. + CI / d	sales lift, share, CLV, margin, retention, brand-equity points
Mediation (process)	which mechanism carries the effect	indirect effect + bias-corrected CI	why the process matters for the decision
Moderation (contingency)	when the effect strengthens/reverses	interaction + simple slopes	the managerial guardrail / segmentation rule
Robustness / alternative model	which threat (CMV, endogeneity) is reduced	same discipline as the main result	whether the conclusion's direction/size holds

If the managerial-magnitude column is empty, the result is not yet ready for a JAMS results section.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. JAMS is empirical marketing with much survey-based SEM; the chain below serves causal / quasi-experimental designs and many-outcome corrections.

Many outcomes / specifications: romano_wolf (step-down FWER) or benjamini_hochberg — report the adjusted threshold.
OVB sensitivity: oster_delta / sensemakr.
Inference: wild_cluster_bootstrap (few clusters), twoway_cluster / conley; multilevel data → cluster at the right level.
Re-fit off one handle: audit_result(result_id) lists the missing checks and the exact suggest_function for each.
Exhibits: etable / did_summary_to_latex from the handle — no retyped numbers.

Keep the decisive checks in the body and the exhaustive battery in the appendix. See the executed chain in the JF execution walkthrough.

Checklist

Estimator matches design and data structure; SE clustering correct
SEM: measurement model reported before structural; full fit indices given
PLS: HTMT, R², Q², f² reported; paths bootstrapped
Mediation via bootstrapped indirect effects with bias-corrected CIs
Moderation: simple slopes + index of moderated mediation where relevant
Effect sizes and uncertainty reported throughout (APA style)
Every headline result has a managerial-magnitude translation
Robustness addresses the design's specific threat (CMV / endogeneity / pre-trends)

Robustness that targets the design's real threat

Generic robustness ("we also ran model B") rarely persuades JAMS reviewers; the robustness must answer the specific threat to the genre's inference:

Survey/SEM: rule out CMV with a marker-variable / CFA-marker model and report whether paths survive; test an alternative measurement specification; show results hold on a holdout or second sample.
Secondary data: placebo tests, alternative instruments, pre-trend/parallel-trends evidence, sensitivity to the identifying assumption, and alternative fixed-effect structures.
Experiment: replication across stimuli/samples, a confound-ruling-out study, and a test of the alternative-mechanism account.
Meta-analysis: sensitivity to coding decisions, trim-and-fill / PET-PEESE for publication bias, and influence diagnostics for outlier studies.

State, for each robustness check, which threat it neutralizes — a list of checks with no mapped threat reads as box-ticking.

Anti-patterns

Baron–Kenny causal-steps mediation instead of bootstrapped indirect effects
Reporting fit indices but no standardized paths or R²
Significance with no effect size and no managerial magnitude
Ignoring nesting (consumers within stores) and clustering
A weak/untested instrument, or endogeneity waved away
Asterisk tables that hide the size of the effect
Robustness checks listed with no statement of which threat each addresses

Output format

【Design】survey-SEM / PLS / HLM / experiment / panel-causal / choice / meta
【Estimator】matches design? SE clustering: [...]
【Measurement (if SEM/PLS)】AVE/CR/discriminant + fit/HTMT: pass/fix
【Effect sizes + uncertainty】reported (APA)? pass/fix
【Mediation/moderation】bootstrapped indirect / simple slopes: done?
【Managerial-magnitude ledger】every headline result translated? yes/fix
【Robustness】design-specific threat addressed: [...]
【Next skill】jams-contribution-framing